Machine Learning with AMD GPUs

Photo by Lubo Minar on Unsplash

You want to run PyTorch or TensorFlow on your PC, but you have an AMD GPU. No worries, this guide will get you up and running in no time!
Use Ubuntu LTS to get started as soon as possible.

First, install ROCm, the AMD equivalent of CUDA.

Then, install your favorite machine learning library.
The easiest way is to use docker.

For Pytorch, follow this:

For Tensorflow, follow this:

Now to start development, I recommend using Visual Studio Code (VSC).

Method 1:

First, start Jupyter in your container, copy the URI.


Now, configure your VSC to use the remote Jupyter server: for more details, follow this link:

Method 2:

Or, install the “Remote — Containers” extension for VSC.

ext install ms-vscode-remote.remote-containers

Happy hacking!




Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

A Guide to Loss Functions for Deep Learning Classification in Python

Alternative Feature Selection Methods in Machine Learning

Copista: On device high-resolution image style transfer

Diving deeper into Prodigy

Automatic Washing Symbols Detection

Indirect Bias support in IBM Watson OpenScale

PyTorch Module : quick reference

Variational Inference: Gaussian Mixture model

Approximating true posterior over latent parameters with mean field approximation

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Mansoor Aldosari

Mansoor Aldosari

More from Medium

Transfer Learning with Amazon SageMaker and FSx for Lustre

ML Model Optimization for low compute environments


Inference in Production: 5 Factors that Impact It & the Hardware Usage Metrics to Track